Scikit-fingerprints: Python library for scikit-learn compatible molecular fingerprints and chemoinformatics
cs.LG, cs.SE
Submitted: 2026-08-03
Updated: 2026-09-21
Code: https://github.com/rdkit/rdkit
License: http://creativecommons.org/licenses/by/4.0/
The gist: We present scikit-fingerprints, a comprehensive, fully scikit-learn compatible library for molecular machine learning in Python, based on RDKit.
Terminology
Abstract
We present scikit-fingerprints, a comprehensive, fully scikit-learn compatible library for molecular machine learning in Python, based on RDKit. Molecular fingerprints and related functionalities are workhorses of chemoinformatics, yet the widely used open-source frameworks are not compatible with the wider Python machine learning ecosystem based on scikit-learn conventions. scikit-fingerprints closes this gap, bringing molecular fingerprints, molecular filters, similarity and distance measures, applicability domain estimation, data splitting strategies, and more under a single, familiar interface. Scikit-learn compatibility means that an entire chemoinformatics workflow, from a raw SMILES string to a deployable model, can be assembled from composable building blocks and can reuse the mature tooling of the surrounding ecosystem. The underlying RDKit code makes it familiar and extensible for custom chemoinformatics use cases. We put a strong focus on unified interfaces, ease of use, computational efficiency, customization, and extensibility. scikit-fingerprints makes molecular machine learning faster to prototype, easier to reproduce, and simpler to deploy.
Sources
- UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
- HuggingFace's Transformers: State-of-the-art Natural Language Processing
- Deep Learning Foundation Models from Classical Molecular Descriptors
- Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learning
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